Plot Gallery
Workspace and Other Setup
Packages & settings
Load what I need.
library(wso)
library(tidyverse)
library(lubridate)
library(scales)
library(ggrepel)
wso::util.settings()Modify data
To make our data more convenient to work with
miris <- iris %>%
mutate_if(
is.numeric, ~ . * 100
) %>%
mutate(
wsoIndex = 1:nrow(iris)
)
fuelEconomy <- mpg %>%
group_by(year, cyl) %>%
summarize(
avgHwy = mean(hwy)
)
monthlyStorms <- storms %>%
mutate(
year = make_date(year)
) %>%
group_by(year, name) %>%
summarise(
meanPressure = mean(pressure),
meanWind = mean(wind)
) %>%
group_by(year) %>%
summarise(
count = dplyr::n(),
meanPressure = mean(meanPressure),
meanWind = mean(meanWind)
) %>%
mutate(
studyPeriod = if_else(
year >= make_date(2000) & year <= make_date(2010),
TRUE, FALSE
)
) %>%
filter(
year >= make_date(1998) & year <= make_date(2012)
)
huron <- tibble(
year = make_date(1875:1972),
level = as.vector(LakeHuron)
)Themes and colors
Make my own theme!
theme_cs <- theme_minimal() +
theme(
## Title
plot.title = element_text(
size = 14, face = "bold", hjust = 0.5,
margin = margin(t = 10, r = 0, b = 10, l = 0)
),
## Axis
axis.text = element_text(size = 10),
axis.title.x = element_text(margin = margin(t = 10, r = 0, b = 10, l = 0)),
axis.title.y = element_text(margin = margin(t = 0, r = 10, b = 0, l = 10)),
## Legend
legend.title = element_text(face = "bold"),
legend.text = element_text(size = 10)
)Bar
Category on x axis
mpg %>%
group_by(class) %>%
summarise(
avgHwy = mean(hwy)
) %>%
ggplot() +
geom_col(
aes(x = class, y = avgHwy), fill = wso::color.wsoCols("dark blue")
) +
labs(
title = "Fuel Economy by Class",
x = "Class",
y = "MPG"
) +
theme_cs
Reordering
mpg %>%
group_by(class) %>%
summarise(
avgHwy = mean(hwy)
) %>%
ggplot() +
geom_col(
aes(
x = forcats::fct_reorder(class, avgHwy),
y = avgHwy
), fill = wso::color.wsoCols("dark blue")
) +
labs(
title = "Fuel Economy by Class",
x = "Class",
y = "MPG"
) +
theme_cs +
theme(
axis.text.x = element_text(angle = 45, hjust = 1)
)
Switch the order, add some labels, and make sure our y axis tops out over our maximum value.
cars <- mpg %>%
group_by(class) %>%
summarise(
avgHwy = mean(hwy)
)
cars %>%
ggplot() +
geom_col(
aes(
x = forcats::fct_reorder(class, avgHwy, .desc = TRUE),
y = avgHwy
), fill = wso::color.wsoCols("dark blue")
) +
scale_y_continuous(
limits = c(0, plyr::round_any(max(cars$avgHwy), 10, ceiling))
) +
geom_text(
aes(
x = forcats::fct_reorder(class, avgHwy, .desc = TRUE),
y = avgHwy - 2,
label = scales::comma_format(.01)(avgHwy)
),
size = 3, colour = "#FEFFFE", fontface = "bold"
) +
labs(
title = "Fuel Economy by Class",
x = "Class",
y = "MPG"
) +
theme_cs +
theme(
axis.text.x = element_text(angle = 45, hjust = 1)
)
Date on x axis
monthlyStorms %>%
ggplot() +
geom_col(
aes(x = year, y = count), fill = wso::color.wsoCols("dark blue")
) +
scale_x_date(
labels = scales::date_format("%Y"),
date_breaks = "3 years"
) +
labs(
title = "Storms in US Per Year",
x = "Year",
y = "Count of storms"
) +
theme_cs
Fill by Group
monthlyStorms %>%
ggplot() +
geom_col(
aes(x = year, y = count, fill = studyPeriod)
) +
scale_fill_manual(
name = "Event",
breaks = c("TRUE", "FALSE"),
labels = c("In Study Period", "Out of Study Period"),
values = c(
"TRUE" = unname(wso::color.wsoCols("dark blue")),
"FALSE" = unname(wso::color.wsoCols("light grey"))
)
) +
labs(
title = "Storms in US Per Year",
x = "Year",
y = "Count of storms"
) +
theme_cs
Stacked
storms %>%
ggplot() +
geom_bar(
aes(x = make_date(year), fill = status)
) +
wso::color.scale_fill_wso(
name = "",
labels = stringr::str_to_title
) +
labs(
title = "Storms in US Per Year",
x = "Year",
y = "Count of storms"
) +
theme_cs +
theme(
legend.position = "bottom"
)
Stacked (Proportions)
storms %>%
ggplot() +
geom_bar(
aes(x = make_date(year), fill = status),
position = "fill"
) +
scale_y_continuous(
labels = scales::percent_format(1)
) +
wso::color.scale_fill_wso(
name = "",
labels = stringr::str_to_title
) +
labs(
title = "Type of storms in US Per Year",
x = "Year",
y = "Percent"
) +
theme_cs +
theme(
legend.position = "bottom"
)
Side by side
fuelEconomy %>%
ggplot(
## Include in here so it is given to all layers
## Make cyl a factor so we can use with bar chart
aes(x = as.factor(year), y = avgHwy, fill = as.factor(cyl))
) +
geom_bar(
# Put the bars of different groups next to each other instead of stacked
position = "dodge", stat = "identity"
) +
# Gives us a little breathing room up to so our labels aren't cut off
geom_blank(
aes(y = avgHwy * 1.1)
) +
labs(
title = "Fuel Economy Over the Years",
y = "Fuel Economy (mpg)",
x = "Year"
) +
wso::color.scale_fill_wso(
name = "Drive Type"
) +
theme_cs
Now adjust the column names and add labels to make sure our legend colors and plots fill colors match.
fuelEconomy %>%
ggplot(
aes(x = as.factor(year), y = avgHwy, fill = as.factor(cyl))
) +
geom_bar(
position = "dodge", stat = "identity"
) +
geom_text(
# Specify data for annotations
aes(label = factor(cyl)),
# Space the labels so they aren't stacked
position = position_dodge(width = 0.9),
# Bump the annotation up
vjust = -1
) +
# Gives us a little breathing room up to so our labels aren't cut off
geom_blank(
aes(y = avgHwy * 1.1)
) +
labs(
title = "Fuel Economy Over the Years",
y = "Fuel Economy (mpg)",
x = "Year"
) +
wso::color.scale_fill_wso(
name = "Drive Type",
palette = "dark blue to green",
interpolate = TRUE,
labels = c("Four", "Five", "Six", "Eight")
#labels = c("Eight", "Six", "Five", "Four") ## Watch out this is out of order and you wouldn't know it
) +
theme_cs
Sideways
mysleep <- msleep %>%
arrange(desc(sleep_total)) %>%
slice(50:70) %>%
mutate(
wellRested = if_else(
sleep_total >= 8, "> 8 Hours", "< 8 Hours"
)
)
mysleep %>%
ggplot() +
geom_col(
aes(
x = forcats::fct_reorder(name, sleep_total),
y = sleep_total,
fill = wellRested
)
) +
coord_flip() +
labs(
title = "How well rested are these animals?",
y = "Hours of sleep",
x = "Animal"
) +
scale_y_continuous(
breaks = seq(0, max(mysleep$sleep_total), 2)
) +
scale_fill_manual(
name = "Hours of \nsleep per day",
breaks = c( "> 8 Hours", "< 8 Hours"),
values = c(
"> 8 Hours" = unname(wso::color.wsoCols("dark blue")),
"< 8 Hours" = unname(wso::color.wsoCols("burnt orange"))
)
) +
theme_cs
Point / Scatter
Basic
miris %>%
ggplot() +
geom_point(
aes(x = Sepal.Width, y = Petal.Width, color = Species),
size = 3
) +
scale_x_continuous(
labels = scales::comma_format()
) +
scale_y_continuous(
labels = scales::comma_format()
) +
labs(
title = "Sepal Width by Petal Length",
x = "Sepal.Width (small units)",
y = "Petal.Length (small units)"
) +
wso::color.scale_color_wso() +
theme_cs
Annotate specific data points
selectGroup <- miris %>%
filter(
Species == "virginica",
Sepal.Width > 3750
)
selectPoint <- miris %>%
filter(
Petal.Width + Sepal.Width == min(Petal.Width + Sepal.Width)
)
miris %>%
ggplot(
aes(x = Petal.Length, y = Sepal.Width)
) +
geom_point(
aes(color = Species), size = 3
) +
geom_point(data = selectGroup, size = 3, shape = 1) +
geom_point(data = selectPoint, size = 3, shape = 1) +
geom_text_repel(data = selectGroup, mapping = aes(label = wsoIndex), point.padding = 1) +
geom_label_repel(data = selectPoint, mapping = aes(label = wsoIndex), point.padding = 1) +
scale_x_continuous(
labels = scales::comma_format()
) +
scale_y_continuous(
labels = scales::comma_format()
) +
labs(
title = "Sepal Width by Petal Length",
x = "Petal.Length (small units)",
y = "Sepal.Width (small units)"
) +
wso::color.scale_color_wso() +
theme_cs
Legend label adjustments
storms %>%
ggplot() +
geom_point(
aes(x = wind, y = pressure, color = factor(status))
) +
labs(
title = "Pressure vs Wind Speed of Storms",
y = "Pressure (mbar)",
x = "Wind Speed (mph)"
) +
wso::color.scale_color_wso(
name = "Storm Type",
labels = stringr::str_to_title
) +
theme_cs
Jitter
mpg %>%
ggplot() +
geom_jitter(
aes(x = cty, y = hwy, color = class),
width = 0.5, height = 0.5
) +
wso::color.scale_color_wso(
name = "Class",
labels = stringr::str_to_title
) +
labs(
title = "Fuel Economy",
y = "Highway MPG",
x = "City MPG"
) +
theme_cs
Lump together classes that appear less than 15% of the time
mpg %>%
ggplot() +
geom_jitter(
aes(
x = cty,
y = hwy,
color = forcats::fct_lump(class, prop = 0.15)
),
width = 2, height = 2
) +
wso::color.scale_color_wso(
name = "Class",
labels = stringr::str_to_title
) +
labs(
title = "Fuel Economy",
y = "Highway MPG",
x = "City MPG"
) +
theme_cs
Count
mpg %>%
ggplot() +
geom_count(
aes(x = cty, y = hwy), color = wso::color.wsoCols("dark blue")
) +
labs(
title = "Fuel Economy",
y = "Highway MPG",
x = "City MPG"
) +
theme_cs
Rug
mtcars %>%
ggplot(
aes(x = wt, y = mpg)
) +
geom_point(
color = wso::color.wsoCols("blue")
) +
geom_rug() +
coord_cartesian(
ylim = c(0, plyr::round_any(max(mtcars$mpg), 10, f = ceiling))
) +
labs(
title = "Fuel Economy by Weight",
y = "Highway MPG",
x = "Weight (tons)"
) +
theme_cs
Regression
Manual
model <- lm(weight ~ Time, data = ChickWeight)
int <- coef(model)["(Intercept)"] %>% unname()
slope <- coef(model)["Time"] %>% unname()
ChickWeight %>%
ggplot() +
geom_abline(
intercept = int, slope = slope, color = wso::color.wsoCols("dark blue"),
) +
geom_point(
aes(x = Time, y = weight, color = Diet)
) +
labs(
title = "Weight of Chicks by Diet Type",
x = "Days from birth",
y = "Weight (grams)"
) +
theme_cs
Geom Smooth
ChickWeight %>%
ggplot(
aes(x = Time, y = weight, color = Diet)
) +
geom_smooth(
se = FALSE, method = lm
) +
geom_point() +
labs(
title = "Weight of Chicks by Diet Type",
x = "Days from birth",
y = "Weight (grams)"
) +
theme_cs
Box plot
ChickWeight %>%
ggplot() +
geom_boxplot(
aes(x = Diet, y = weight),
outlier.colour = "red", outlier.shape = 1
) +
labs(
title = "Chick Weight By Diet",
x = "Diet",
y = "Weight (grams)"
) +
theme_cs
With the original data
ChickWeight %>%
ggplot(
aes(x = Diet, y = weight)
) +
geom_jitter(
aes(color = Diet)
) +
geom_boxplot(
outlier.shape = NA, alpha = 0
) +
labs(
title = "Chick Weight By Diet",
x = "Diet",
y = "Weight (grams)"
) +
theme_cs
Histogram
diamonds %>%
ggplot() +
geom_histogram(
aes(x = price), fill = wso::color.wsoCols("dark blue"),
bins = 35
# binwidth = 0.05 ## Could use this too
) +
scale_y_continuous(
labels = scales::comma_format()
) +
scale_x_continuous(
labels = scales::dollar_format()
) +
labs(
title = "Diamonds",
x = "Price",
y = "Count"
) +
theme_cs
diamonds %>%
ggplot() +
geom_histogram(
aes(x = price, fill = cut),
bins = 35
# binwidth = 0.05 ## Could use this too
) +
wso::color.scale_fill_wso(
name = "Price"
) +
scale_y_continuous(
labels = scales::comma_format()
) +
scale_x_continuous(
labels = scales::dollar_format()
) +
labs(
title = "Diamonds",
x = "Price",
y = "Count"
) +
theme_cs
Frequency
diamonds %>%
ggplot() +
geom_freqpoly(
aes(x = price, color = cut),
bins = 35
# binwidth = 0.05 ## Could use this too
) +
wso::color.scale_color_wso(
name = "Price"
) +
scale_y_continuous(
labels = scales::comma_format()
) +
scale_x_continuous(
labels = scales::dollar_format()
) +
labs(
title = "Diamonds",
x = "Price",
y = "Count"
) +
theme_cs
diamonds %>%
ggplot() +
geom_freqpoly(
aes(x = price, color = cut, stat(density)),
bins = 35
# binwidth = 0.05 ## Could use this too
) +
wso::color.scale_color_wso(
name = "Price"
) +
scale_y_continuous(
labels = scales::percent_format(.01)
) +
scale_x_continuous(
labels = scales::dollar_format()
) +
labs(
title = "Diamonds",
x = "Price",
y = "Percent"
) +
theme_cs
Density
diamonds %>%
ggplot() +
geom_density(
aes(x = depth, color = cut, fill = cut),
alpha = 0.1
) +
coord_cartesian(
xlim = c(55, 70)
) +
wso::color.scale_color_wso(
name = "Cut"
) +
wso::color.scale_fill_wso(
name = "Cut"
) +
scale_y_continuous(
labels = scales::percent_format(.01)
) +
labs(
title = "Diamonds",
x = "Depth",
y = "Percent"
) +
theme_cs +
theme(
legend.position = "bottom"
)
Facets
With historgrams and a labeller function
classList <- c(
"compact" = "Compact",
"midsize" = "Midsize",
"suv" = "Sports Utility Vehicle (SUV)"
)
yearList <- c(
"1999" = "1999",
"2008" = "2008"
)
labelFunction = as_labeller(c(classList, yearList))
mpg %>%
filter(
class %in% c("compact", "midsize", "suv")
) %>%
ggplot() +
geom_histogram(
aes(x = cty),
bins = 35,
fill = wso::color.wsoCols("dark blue")
) +
facet_grid(
vars(year), vars(class),
labeller = labelFunction
) +
labs(
title = "Fuel Economy",
y = "Count of vehicles",
x = "City MPG"
) +
theme_cs
With a scatterplot and spaced facets
miris %>%
ggplot() +
geom_point(
aes(x = Sepal.Width, y = Petal.Width, color = Species),
size = 2
) +
facet_grid(
cols = vars(Species)
) +
scale_x_continuous(
labels = scales::comma_format()
) +
scale_y_continuous(
labels = scales::comma_format()
) +
labs(
title = "Sepal Width by Petal Length",
x = "Sepal.Width (small units)",
y = "Petal.Length (small units)"
) +
wso::color.scale_color_wso() +
theme_cs +
theme(
panel.spacing = unit(2, "lines"),
strip.background = element_blank(),
strip.text.x = element_blank(),
legend.position = "bottom",
axis.text.x = element_text(angle = 45, hjust = 1)
)
Error Bars
Path
economics <- economics %>%
mutate(
unemployRate = unemploy/pop
)
economics %>%
ggplot() +
geom_path(
mapping = aes(unemployRate, y = psavert, color = as.numeric(date)),
lineend = "butt", linejoin = "round", linemitre = 1
) +
wso::color.scale_color_wso(
discrete = FALSE,
palette = "dark blue to green",
name = "Year",
labels = function(label) {
labelDate <- as.Date(label, origin = "1970-01-01")
year(labelDate)
}
) +
labs(
title = "Unemployment vs Personal Savings Over Time",
x = "Unemployment Rate",
y = "Personal Savings"
) +
theme_cs
Polygon
ids <- factor(c("A", "B", "C", "D", "E", "F"))
values <- tibble(
id = ids,
value = c(3, 3.1, 3.1, 3.2, 3.15, 3.5)
)
positions <- tibble(
id = rep(ids, each = 4),
x = c(2, 1, 1.1, 2.2, 1, 0, 0.3, 1.1, 2.2, 1.1, 1.2, 2.5, 1.1, 0.3,
0.5, 1.2, 2.5, 1.2, 1.3, 2.7, 1.2, 0.5, 0.6, 1.3),
y = c(-0.5, 0, 1, 0.5, 0, 0.5, 1.5, 1, 0.5, 1, 2.1, 1.7, 1, 1.5,
2.2, 2.1, 1.7, 2.1, 3.2, 2.8, 2.1, 2.2, 3.3, 3.2)
)
datapoly <- left_join(values, positions, by = "id")
datapoly %>%
ggplot() +
geom_polygon(
aes( x = x, y = y, fill = value, group = id)
) +
wso::color.scale_fill_wso(
discrete = FALSE,
palette = "blue green red",
name = "Score"
) +
labs(
title = "No Clue What This Is",
x = "A crazy variable",
y = "Something wild"
) +
theme_cs
Rectangle (Background Color)
Horizontal (Data outside ggplot)
Note that here we are defining our data outside of our main geom so that we can properly apply an alpha. Details here :https://stackoverflow.com/questions/17521438/geom-rect-and-alpha-does-this-work-with-hard-coded-values
Also note that with dates in the x-axis, we need to keep xmin and xmax out of aes() because it would try to coerce Inf as a date, which will not work. Alternatively, we could have specified min(monthlyStorms$date) and max(monthlyStorms$date) in our dataframe and kept xmin and xmax in our aes(), but then the colored background doesn’t extend to the edge of the plot, it only goes to the middle of the first and last bar, which isn’t the greatest looking thing in the world.
ggplot() +
geom_rect(
aes(ymin = -Inf, ymax = 8000),
xmin = -Inf, xmax = Inf,
alpha = 0.25, fill = wso::color.wsoCols("green")
) +
geom_line(
data = economics,
aes(x = date, y = unemploy),
color = wso::color.wsoCols("dark blue"), size = 0.75
) +
scale_y_continuous(
label = scales::comma_format(1)
) +
labs(
title = "US Unemployment",
x = "Date",
y = "People Out of Work"
) +
theme_cs
Horizontal (Data insde ggplot)
To avoid having to specify your data in every layer, one work around is to provide a single row from the data set in your rectangle. This will prevent the rectangle from appearing once for each time in your dataset (which prevents you from being able to use alpha).
economics %>%
ggplot() +
geom_rect(
data = economics[1,],
aes(xmin = min(economics$date), xmax = max(economics$date), ymin = 8000, ymax = Inf),
alpha = 0.25, fill = wso::color.wsoCols("red")
) +
geom_line(
aes(x = date, y = unemploy),
color = wso::color.wsoCols("dark blue"), size = 0.75
) +
scale_y_continuous(
label = scales::comma_format(1)
) +
labs(
title = "US Unemployment",
x = "Date",
y = "People Out of Work"
) +
theme_cs
Horizontal (with data frame)
## No x min or max here because we want to use Inf, and can't make that jive with our date axis
## We'll specify them outside of aes in our geom_rect
backgrounds <- tibble(
minY = c(0, 15),
maxY = c(15, Inf),
fillColor = c("Average", "Abnormal")
)
monthlyStorms %>%
ggplot() +
geom_rect(
data = backgrounds,
aes(ymin = minY, ymax = maxY, fill = fillColor),
xmin = -Inf, xmax = Inf,
alpha = .2
) +
geom_col(
aes(x = year, y = count), fill = unname(wso::color.wsoCols("dark blue"))
) +
scale_fill_manual(
name = "Frequency",
aesthetics = c("fill"),
values = c(
"Average" = unname(wso::color.wsoCols("green")),
"Abnormal" = unname(wso::color.wsoCols("red"))
)
) +
labs(
title = "Storms in US Per Year",
x = "Year",
y = "Count of storms"
) +
theme_cs
Vertical (by value)
Here we use dplyr::lead(date) to dynamically set our xmax… basically this grabs the next value in line.
economicsAdj <- economics %>%
mutate(
status = if_else(
unemploy > 8000,
"Recession", "Boon"
)
)
economicsAdj %>%
ggplot() +
geom_rect(
aes(
xmin = date, xmax = dplyr::lead(date),
ymin = -Inf, ymax = Inf,
fill = status
),
alpha = 0.4
) +
geom_line(
aes(x = date, y = unemploy),
color = wso::color.wsoCols("dark blue"), size = 0.75
) +
scale_y_continuous(
label = scales::comma_format(1)
) +
labs(
title = "US Unemployment",
x = "Date",
y = "People Out of Work"
) +
theme_cs
Ribbon
huron %>%
ggplot() +
geom_ribbon(
aes(x = year, ymin = level - 1, ymax = level + 1),
fill = wso::color.wsoCols("blue"), alpha = 0.4
) +
geom_line(
aes(x = year, y = level),
color = wso::color.wsoCols("dark blue"), size = 1
) +
labs(
title = "Level of Lake Huron",
x = "Date",
y = "Level (ft)"
) +
theme_cs
Lines
Vertical
ChickWeight %>%
ggplot() +
geom_vline(
xintercept = 15, color = wso::color.wsoCols("dark blue")
) +
geom_point(
aes(x = Time, y = weight, color = Diet)
) +
labs(
title = "Weight of Chicks by Diet Type",
x = "Days from birth",
y = "Weight (grams)"
) +
theme_cs
Vertical (repeated)
ChickWeight %>%
ggplot() +
geom_vline(
xintercept = seq(1, 20, 5), color = wso::color.wsoCols("dark blue"),
linetype = "longdash", size = 0.75
) +
geom_point(
aes(x = Time, y = weight, color = Diet)
) +
labs(
title = "Weight of Chicks by Diet Type",
x = "Days from birth",
y = "Weight (grams)"
) +
theme_cs
Horizontal
For annotation, hjust stands for horizontal justification, 0 will be left-justified, 0.5 will be centered, and 1 will be right-justified. For vjust, 0 will be top-justified, 0.5 will be centered, and 1 will be bottom-justified.
ChickWeight %>%
ggplot() +
geom_hline(
yintercept = 100, color = wso::color.wsoCols("red")
) +
geom_point(
aes(x = Time, y = weight, color = Diet)
) +
annotate(
"text", x = 0, y = 105, color = "red", fontface = "bold",
hjust = 0, vjust = 0,
label = "Healthy min. at 15 days"
) +
labs(
title = "Weight of Chicks by Diet Type",
x = "Days from birth",
y = "Weight (grams)"
) +
theme_cs
Sloped
Manually set
ChickWeight %>%
ggplot() +
geom_abline(
intercept = 40, slope = 4, color = wso::color.wsoCols("dark blue"),
) +
geom_point(
aes(x = Time, y = weight, color = Diet)
) +
labs(
title = "Weight of Chicks by Diet Type",
x = "Days from birth",
y = "Weight (grams)"
) +
theme_cs
Area
huron %>%
ggplot() +
geom_area(
aes(x = year, y = level ),
fill = wso::color.wsoCols("blue")
) +
labs(
title = "Level of Lake Huron",
x = "Date",
y = "Level (ft)"
) +
theme_cs
We can also adjust the canvas to zoom in on the changes
huron %>%
ggplot() +
geom_area(
aes(x = year, y = level ),
fill = wso::color.wsoCols("blue")
) +
labs(
title = "Level of Lake Huron",
x = "Date",
y = "Level (ft)"
) +
coord_cartesian(
ylim = c(560, 590)
# Could also set dynamically
#ylim = c(min(huron$level) - 10, max(huron$level + 10))
) +
theme_cs
Random adjustments
Lots of information here: https://ggplot2.tidyverse.org/reference/theme.html
Legend inside plot
storms %>%
ggplot() +
geom_point(
aes(x = wind, y = pressure, color = factor(status))
) +
scale_y_continuous(
labels = scales::comma_format()
) +
labs(
title = "Pressure vs Wind Speed of Storms",
y = "Pressure (mbar)",
x = "Wind Speed (mph)"
) +
wso::color.scale_color_wso(
name = "Storm Type",
labels = stringr::str_to_title
) +
theme_cs +
theme(
legend.position = c(.05, .05),
legend.justification = c("left", "bottom"),
legend.box.just = "right",
legend.margin = margin(6, 6, 6, 6),
legend.background = element_rect(fill = alpha("white", 0.6))
)
Remove legend
storms %>%
ggplot() +
geom_point(
aes(x = wind, y = pressure, color = factor(status)),
show.legend = FALSE
) +
scale_y_continuous(
labels = scales::comma_format()
) +
labs(
title = "Pressure vs Wind Speed of Storms",
y = "Pressure (mbar)",
x = "Wind Speed (mph)"
) +
wso::color.scale_color_wso(
name = "Storm Type",
labels = stringr::str_to_title
) +
theme_cs
Remove Axis
storms %>%
ggplot() +
geom_point(
aes(x = wind, y = pressure, color = factor(status))
) +
labs(
title = "Pressure vs Wind Speed of Storms"
) +
wso::color.scale_color_wso(
name = "Storm Type",
labels = stringr::str_to_title
) +
theme_cs +
theme(
legend.position = "bottom",
axis.title = element_blank(),
axis.text = element_blank(),
axis.ticks = element_blank(),
# axis.title.x = element_blank(),
# axis.text.x = element_blank(),
# axis.ticks.x = element_blank(),
# axis.title.y = element_blank(),
# axis.text.y = element_blank(),
# axis.ticks.y = element_blank()
)
Remove Grid
storms %>%
ggplot() +
geom_point(
aes(x = wind, y = pressure, color = factor(status))
) +
scale_y_continuous(
labels = scales::comma_format()
) +
labs(
title = "Pressure vs Wind Speed of Storms",
y = "Pressure (mbar)",
x = "Wind Speed (mph)"
) +
wso::color.scale_color_wso(
name = "Storm Type",
labels = stringr::str_to_title
) +
theme_cs +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()
)
Remove Everything
storms %>%
ggplot() +
geom_point(
aes(x = wind, y = pressure, color = factor(status)),
show.legend = FALSE
) +
wso::color.scale_color_wso() +
theme_cs +
theme_void()
Adjust Grid
storms %>%
ggplot() +
geom_point(
aes(x = wind, y = pressure, color = factor(status))
) +
labs(
title = "Pressure vs Wind Speed of Storms",
y = "Pressure (mbar)",
x = "Wind Speed (mph)"
) +
wso::color.scale_color_wso(
name = "Storm Type",
labels = stringr::str_to_title
) +
theme_cs +
theme(
panel.grid.minor = element_line(
colour = wso::color.wsoCols("dark blue"),
size = 0.25
)
# panel.grid.major = element_line(
# colour = wso::color.wsoCols("dark blue"),
# size = 0.25
# )
)